期刊名称：International Journal of Signal Processing, Image Processing and Pattern Recognition
印刷版ISSN：2005-4254
出版年度：2016
卷号：9
期号：11
页码：309
出版社：SERSC
摘要：Aiming at the traditional GEP algorithm adopted fixed rate of mutation and crossover rate in the process of evolution, and ignored the dynamic change of individual fitness, which leaded to the presence of premature convergence and local optimization problem. By using the cloud adaptive strategy and cloud cross strategy of cloud model, a genetic algorithm based on cloud model(Cloud Model Gene Expression Programming,CMGEP) was proposed. The algorithm adjusted the mutation rate and crossover rate in evolution through the cloud adaptation strategy according to the change of dynamic，and timely calculated population similarity to achieve cloud cross to increase the diversity of population and jump out of the premature convergence. It was applied to the field of railway engineering and its results werecompared with those obtained by traditional GEP Algorithm and CMGEP Algorithm. Experiments show that the algorithm can improve the adaptability and the prediction accuracy, it has better convergence.
